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   "id": "f4cf78e1",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Densely Connected Networks (DenseNet)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "898d7836",
   "metadata": {
    "execution": {
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    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb6db8a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Dense Blocks"
   ]
  },
  {
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   "execution_count": 3,
   "id": "805394e3",
   "metadata": {
    "execution": {
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     "pytorch"
    ]
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   "outputs": [],
   "source": [
    "def conv_block(num_channels):\n",
    "    return nn.Sequential(\n",
    "        nn.LazyBatchNorm2d(), nn.ReLU(),\n",
    "        nn.LazyConv2d(num_channels, kernel_size=3, padding=1))\n",
    "\n",
    "class DenseBlock(nn.Module):\n",
    "    def __init__(self, num_convs, num_channels):\n",
    "        super(DenseBlock, self).__init__()\n",
    "        layer = []\n",
    "        for i in range(num_convs):\n",
    "            layer.append(conv_block(num_channels))\n",
    "        self.net = nn.Sequential(*layer)\n",
    "\n",
    "    def forward(self, X):\n",
    "        for blk in self.net:\n",
    "            Y = blk(X)\n",
    "            X = torch.cat((X, Y), dim=1)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff6dae74",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Define a `DenseBlock` instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e369c1ad",
   "metadata": {
    "execution": {
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    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 23, 8, 8])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blk = DenseBlock(2, 10)\n",
    "X = torch.randn(4, 3, 8, 8)\n",
    "Y = blk(X)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc32fbb1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Transition Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6160cc48",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-08-18T19:47:28.919184Z"
    },
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    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def transition_block(num_channels):\n",
    "    return nn.Sequential(\n",
    "        nn.LazyBatchNorm2d(), nn.ReLU(),\n",
    "        nn.LazyConv2d(num_channels, kernel_size=1),\n",
    "        nn.AvgPool2d(kernel_size=2, stride=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f88387c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Apply a transition layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc0cacfc",
   "metadata": {
    "execution": {
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    "tab": [
     "pytorch"
    ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 10, 4, 4])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blk = transition_block(10)\n",
    "blk(Y).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ea910e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "DenseNet Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "58137883",
   "metadata": {
    "execution": {
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    "tab": [
     "pytorch"
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   "outputs": [],
   "source": [
    "class DenseNet(d2l.Classifier):\n",
    "    def b1(self):\n",
    "        return nn.Sequential(\n",
    "            nn.LazyConv2d(64, kernel_size=7, stride=2, padding=3),\n",
    "            nn.LazyBatchNorm2d(), nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2, padding=1))\n",
    "\n",
    "@d2l.add_to_class(DenseNet)\n",
    "def __init__(self, num_channels=64, growth_rate=32, arch=(4, 4, 4, 4),\n",
    "             lr=0.1, num_classes=10):\n",
    "    super(DenseNet, self).__init__()\n",
    "    self.save_hyperparameters()\n",
    "    self.net = nn.Sequential(self.b1())\n",
    "    for i, num_convs in enumerate(arch):\n",
    "        self.net.add_module(f'dense_blk{i+1}', DenseBlock(num_convs,\n",
    "                                                          growth_rate))\n",
    "        num_channels += num_convs * growth_rate\n",
    "        if i != len(arch) - 1:\n",
    "            num_channels //= 2\n",
    "            self.net.add_module(f'tran_blk{i+1}', transition_block(\n",
    "                num_channels))\n",
    "    self.net.add_module('last', nn.Sequential(\n",
    "        nn.LazyBatchNorm2d(), nn.ReLU(),\n",
    "        nn.AdaptiveAvgPool2d((1, 1)), nn.Flatten(),\n",
    "        nn.LazyLinear(num_classes)))\n",
    "    self.net.apply(d2l.init_cnn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab4d03b5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef87c44e",
   "metadata": {
    "execution": {
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      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DenseNet(lr=0.01)\n",
    "trainer = d2l.Trainer(max_epochs=10, num_gpus=1)\n",
    "data = d2l.FashionMNIST(batch_size=128, resize=(96, 96))\n",
    "trainer.fit(model, data)"
   ]
  }
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